{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "from ggplot import *"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### `scale_x_date`\n",
    "`scale_x_date` scale a continuous x-axis. Its parameters are:\n",
    "\n",
    "- `name` - axis label\n",
    "- `breaks` - x tick breaks\n",
    "- `labels` - x tick labels\n",
    "\n",
    "- `date_format` - date string formatter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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GxkbNtVEoFGCxWLBgwQLF6263Wxyr3BeBQAAbN27UDLDN5XKKmt/jDRbbDMMw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\n77//Pv7whz9g27ZtAJQDIhc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5KBQKSCaThjESl8uFzZs3o7m5WSxTD5C0Wq2IxWK69Z0L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KAAAg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\nR1F2cQI61wAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10f53bd90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<ggplot: (285186569)>"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ggplot(meat, aes('date','beef')) + \\\n",
    "    geom_line() + \\\n",
    "    scale_x_date(breaks=date_breaks('10 years'),\n",
    "                 labels=date_format('%B %-d, %Y'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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Tz2bt2CgBN2z36dPntOvDhg077XqXLl3UpUuXU9ajo6PL7tMNoGqbP3++vvzy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\nBARo6dKl2rVr1zn3a+4ykfLyclVWVja5rba2VsH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3m81mNb7es/E26cyLMaxWq5544gk5\nHA69/PLLro/5+/s3ed5evXopOztblZWVSkhIMGz/GiUkJLieZ8OGDbLZbCopKVFpaanmzp0r6cxb\nuaampurRRx+V1Wp1fZF26tRJ4eHhKi4uVqdOna7o+dtyzlLTJQ29e/fWokWLJJ05+tF4mvNqc27r\nGdtsNsXHx0s68wPIZDLpxx9/VFBQEBlfpgtlLJ35pWbfvn1KTk523WaxWNz2/aKtZ5yTk6MRI0ZI\nkrp3766VK1dK4nvFlbhQxtdcc40mTZok6cwZscYlKGR8YQUFBVq1apUefPBB19d6SEiIysrKXPcp\nLy93ZWy1WlVZWSmr1aqKigrXz0B3ZoxztdhlJDU1NQoODpbZbFZubm6z1jGFhYXp+PHjks6cgmp0\n9vq6nTt36mIXYOnWrZv+85//6Pjx465TMudzvsfYvn27tm/ffsk5z9Z4Cqe0tFT79u3Trbfeqg4d\nOui3v/2tUlJSlJKSIpvNpqlTp8pqtaqqqkoOh0PSmVNeJSUlstvtl/WcZ2vLOUtyrXGTpH379ikq\nKsp1f3fl3NYz7tatm3JzcyWduUKBw+FQUFAQGbsxY0k6dOiQIiMjmxRwMr76jBtP99tsNh05ckSS\ndPjwYUVERLjuT8bu+TxuvN3hcCgjI0M/+clPXLeT8bkZl5aWaunSpbrvvvuaLB2Ljo52HbSrr6/X\n7t27FRcXJ0mKi4tTTk6Oa+bG293dLdBUizuy7XA45Ofnp1tvvVWLFi3S3Llz1alTJ0VGRl5y23vu\nuUdpaWnasWOHunbt6rq9T58+Wrp0qXbu3KkuXbqc8xvn2SwWi2688UYFBAScc4q20SeffKIjR46o\nurpas2fP1sCBA5WQkKCioiJdf/3159y/srJS8+bNU01NjUwmk7788kvX6ZmPP/5Y1dXVrreUP/sq\nAmdr/ALMy8vTxo0bZbFYZDKZNHr06Cs6HUXOZ3Jev369CgoKZDKZFBYWpjFjxkhyT85kfCbjhIQE\nrVixQm+//bYsFovuu+8+SWTs7u8Xe/bsUY8ePZo8FhlffcaNp9HHjBmjtWvXyul0ys/Pj+8VBnwe\nf/vtt/rqq68kSfHx8a4jwGR8/owzMjJUXV2tNWvWSDpzBL7xKi8jR47UggUL5HQ6lZCQ4NrPvn37\nKi0tTdnZ2QoNDXVdychd3QIX4GxhTpw44Zw3b57Xnr+hocE5d+5cZ3Fx8WVv+9FHHznr6+sNmMr9\nyNl4ZGw8MjYeGRuPjI1HxvCmFvWmNl9//bW2bdumESNGKDY21uPPX1hYqEWLFik+Pl5Dhw71+PN7\nCjkbj4yNR8bGI2PjkbHxyBje1qLKNgAAANCatNgXSAIAAAC+jrINAAAAGISyDQAAABiEsg0AAAAY\nhLINAAAAGISyDQAAABiEsg0AAAAYhLINAAAAGISyDQAAABiEsg0AAAAYhLINAAAAGISyDQAAABiE\nsg0AAAAYhLINAAAAGISyDQAAABiEsg0AAAAYhLINAAAAGISyDQA+5IYbblBQUJBCQ0MVHh6uxMRE\npaamyul0XnLbvLw8mc1mORwOD0wKAJAo2wDgU0wmk9asWaOysjLl5eXphRde0KxZs/TrX//6kts6\nnU6ZTKZmFXMAgHtQtgHAxzSW5ZCQEI0ePVoff/yxPvjgA+3du1eff/65evXqpdDQUHXu3Fn/93//\n59quf//+kqSwsDDZbDZt27ZNkvT+++/rlltuUUREhEaMGKGjR496fqcAoJWibAOAj+vTp49iYmKU\nmZkpq9WqBQsWqKysTGvWrNE777yjlStXSpIyMjIkSeXl5SovL9edd96pFStW6C9/+YuWL1+uwsJC\nJSUlaeLEid7cHQBoVSjbANAKdOrUSSUlJerXr5+6d+8uSerRo4cmTJigzZs3N7nv2ctIUlNT9bvf\n/U4333yzzGazXnjhBeXk5OjYsWMenR8AWivKNgC0Avn5+QoPD9f27ds1aNAgRUVFKSwsTKmpqSoq\nKrrgdnl5eZoxY4bCw8MVHh6uiIgImUwm5efne3B6AGi9KNsA4OO++uorHT9+XImJiXrggQc0btw4\n5efnq7S0VMnJya4j2SaT6Zxtr7/+eqWmpqqkpEQlJSU6deqUKisrddddd3l6NwCgVaJsA4CPqqio\n0OrVqzVx4kRNmjRJ3bt3V2Vlpex2u/z9/bV9+3YtWrTIdf/IyEiZzWYdOnTIdVtycrJmzpypvXv3\nSpLKysr0ySefeHxfAKC18vP2AACAyzNmzBj5+fnJbDbrlltu0bPPPqvk5GRJ0ttvv63f/OY3evLJ\nJ9W/f3/94he/UGlpqSQpMDBQv//979W3b1/V19dr3bp1GjdunKqqqjRhwgQdPXpUoaGhGjJkiO6/\n/35v7iIAtBomJxdcBQAAAAzBMhIAAADAIJRtAAAAwCCUbQAAAMAglG0AAADAIJRtAAAAwCCUbQAA\nAMAglG0AAADAIJRtAAAAwCCUbQAAAMAg/w9aX3hfEJ58MgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x110239190>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<ggplot: (274880025)>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ggplot(meat, aes(x='date', y='beef')) + \\\n",
    "    stat_smooth(method='loewss', span=0.2, se=False) + \\\n",
    "    scale_x_date(\"Date\", breaks=date_breaks('10 years'), labels=date_format('%B %-d, %Y'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "global name 'np' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m/Users/glamp/miniconda2/lib/python2.7/site-packages/IPython/core/formatters.pyc\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, obj)\u001b[0m\n\u001b[1;32m    697\u001b[0m                 \u001b[0mtype_pprinters\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtype_printers\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    698\u001b[0m                 deferred_pprinters=self.deferred_printers)\n\u001b[0;32m--> 699\u001b[0;31m             \u001b[0mprinter\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpretty\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    700\u001b[0m             \u001b[0mprinter\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mflush\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    701\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mstream\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgetvalue\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/glamp/miniconda2/lib/python2.7/site-packages/IPython/lib/pretty.pyc\u001b[0m in \u001b[0;36mpretty\u001b[0;34m(self, obj)\u001b[0m\n\u001b[1;32m    381\u001b[0m                             \u001b[0;32mif\u001b[0m \u001b[0mcallable\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmeth\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    382\u001b[0m                                 \u001b[0;32mreturn\u001b[0m \u001b[0mmeth\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcycle\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 383\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0m_default_pprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcycle\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    384\u001b[0m         \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    385\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mend_group\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/glamp/miniconda2/lib/python2.7/site-packages/IPython/lib/pretty.pyc\u001b[0m in \u001b[0;36m_default_pprint\u001b[0;34m(obj, p, cycle)\u001b[0m\n\u001b[1;32m    501\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0m_safe_getattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mklass\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'__repr__'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mNone\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32min\u001b[0m \u001b[0m_baseclass_reprs\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    502\u001b[0m         \u001b[0;31m# A user-provided repr. Find newlines and replace them with p.break_()\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 503\u001b[0;31m         \u001b[0m_repr_pprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mp\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcycle\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    504\u001b[0m         \u001b[0;32mreturn\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    505\u001b[0m     \u001b[0mp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbegin_group\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'<'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/glamp/miniconda2/lib/python2.7/site-packages/IPython/lib/pretty.pyc\u001b[0m in \u001b[0;36m_repr_pprint\u001b[0;34m(obj, p, cycle)\u001b[0m\n\u001b[1;32m    692\u001b[0m     \u001b[0;34m\"\"\"A pprint that just redirects to the normal repr function.\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    693\u001b[0m     \u001b[0;31m# Find newlines and replace them with p.break_()\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 694\u001b[0;31m     \u001b[0moutput\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mrepr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    695\u001b[0m     \u001b[0;32mfor\u001b[0m \u001b[0midx\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0moutput_line\u001b[0m \u001b[0;32min\u001b[0m \u001b[0menumerate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0moutput\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msplitlines\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    696\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0midx\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/glamp/miniconda2/lib/python2.7/site-packages/ggplot-0.9.0-py2.7.egg/ggplot/ggplot.pyc\u001b[0m in \u001b[0;36m__repr__\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    101\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    102\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__repr__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 103\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmake\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    104\u001b[0m         \u001b[0;31m# this is nice for dev but not the best for \"real\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    105\u001b[0m         \u001b[0;31m# self.fig.savefig('/tmp/ggplot.png', dpi=160)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/glamp/miniconda2/lib/python2.7/site-packages/ggplot-0.9.0-py2.7.egg/ggplot/ggplot.pyc\u001b[0m in \u001b[0;36mmake\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    543\u001b[0m                             \u001b[0mlayer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0max\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfacetgroup\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_aes\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx_levels\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_aes\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'x'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0munique\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    544\u001b[0m                         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 545\u001b[0;31m                             \u001b[0mlayer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0max\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfacetgroup\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_aes\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    546\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    547\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mapply_limits\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/glamp/miniconda2/lib/python2.7/site-packages/ggplot-0.9.0-py2.7.egg/ggplot/geoms/geom_area.pyc\u001b[0m in \u001b[0;36mplot\u001b[0;34m(self, ax, data, _aes)\u001b[0m\n\u001b[1;32m     23\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mis_date\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0miloc\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     24\u001b[0m             \u001b[0mdtype\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0miloc\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__class__\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 25\u001b[0;31m             \u001b[0mx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtoordinal\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     26\u001b[0m             \u001b[0max\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfill_between\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mymin\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mymax\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mparams\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     27\u001b[0m             \u001b[0mnew_ticks\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[0;32min\u001b[0m \u001b[0max\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_xticks\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mNameError\u001b[0m: global name 'np' is not defined"
     ]
    },
    {
     "data": {
      "image/png": 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cKpVK0d/fH+VyOTZs2BCbN2+e8vn3338/Dh06FBERixYtikceeSRuvfXWmZ8WAIB5o2JI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\nBboAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x110334ed0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ggplot(meat, aes(x='date', ymin='beef - 1000', ymax='beef + 1000')) + \\\n",
    "    geom_area() + \\\n",
    "    scale_x_date(labels=date_format(\"%m/%Y\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "ggplot(pageviews, aes(x='date_hour', y='pageviews')) + \\\n",
    "    geom_point() + \\\n",
    "    scale_x_date(breaks='1 month')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.11"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}
